Gastos Casa
10 julio, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 14/5/2023 | Comida | 69636 | Tami | NA |
| 21/5/2023 | Comida | 71007 | Tami | NA |
| 25/5/2023 | Parafina | 19788 | Tami | NA |
| 27/5/2023 | Electricidad | 49600 | Andrés | NA |
| 27/5/2023 | Comida | 76142 | Tami | NA |
| 31/5/2023 | Netflix | 5940 | Tami | NA |
| 3/6/2023 | Comida | 104031 | Tami | NA |
| 3/6/2023 | Comida | 8475 | Tami | NA |
| 11/6/2023 | Comida | 14377 | Andrés | NA |
| 16/6/2023 | Diosi | 19330 | Tami | Antiparasitario petsu |
| 17/6/2023 | Diosi | 15980 | Tami | Arena Petsu |
| 17/6/2023 | Comida | 49459 | Tami | Supermercado |
| 22/6/2023 | VTR | 22000 | Tami | NA |
| 24/6/2023 | Comida | 51345 | Tami | NA |
| 29/6/2023 | Enceres | 28352 | Tami | Incoludido (confort y detergente) |
| 30/6/2023 | Comida | 6863 | Andrés | choritos etc |
| 30/6/2023 | Electricidad | 49877 | Andrés | enel |
| 30/6/2023 | Agua | 12706 | Andrés | NA |
| 1/7/2023 | Comida | 48400 | Andrés | de la ostia |
| 2/7/2023 | Comida | 70135 | Tami | Supermercado |
| 2/7/2023 | Parafina | 19418 | Tami | NA |
| 4/7/2023 | Comida | 12000 | Andrés | nueces y almendras 500 gr |
| 6/7/2023 | Gas | 68950 | Andrés | lipigas |
| 8/7/2023 | Agua | 12706 | Andrés | NA |
| 8/7/2023 | Comida | 57693 | Tami | Supermercado |
| 9/7/2023 | correo | 8000 | Andrés | correos de chile raul miranda |
| 9/7/2023 | mouse | 51980 | Andrés | NA |
| 9/7/2023 | lamina | 13800 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 6.7881e+08 2 7.0158 0.001 ***
## lag_depvar 8.4234e+10 1 1741.1952 <2e-16 ***
## Residuals 2.8736e+10 594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1076.58 13381.10 0.0163578
## 2-0 28379.677 22768.35 33991.01 0.0000000
## 2-1 21150.839 17818.26 24483.42 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
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## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
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## 26 17692.29 0 24642.71
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## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 442 50613.94 15173.232
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1964.51816 4017.07474 -514.91114 2458.14985 -2923.83275 535.39271
## 8 9 10 11 12 13
## -5631.37281 -1215.33181 -3996.52623 -477.30251 -4990.57240 -1695.88135
## 14 15 16 17 18 19
## -985.65031 298.84705 -3302.98347 -456.28985 -2197.19140 6530.24116
## 20 21 22 23 24 25
## -1526.09497 -1215.19272 1463.03631 -1178.61416 235.29220 1702.76696
## 26 27 28 29 30 31
## -7074.15282 909.43755 8173.19981 484.51479 53.03924 -2337.03839
## 32 33 34 35 36 37
## 1613.98422 4625.62065 1221.96315 2490.35455 -1753.67684 4695.18283
## 38 39 40 41 42 43
## 4355.22044 -2189.03655 -2926.89744 -1090.40915 -10734.33388 7193.79701
## 44 45 46 47 48 49
## 2543.49499 1379.56155 8129.69997 785.13438 6622.22155 6858.62383
## 50 51 52 53 54 55
## -5691.97099 -4684.59874 -5008.04293 -7931.09104 6052.61866 -4085.34447
## 56 57 58 59 60 61
## -4940.32441 3769.45876 849.44449 -56.82212 120.72806 -5013.46465
## 62 63 64 65 66 67
## 18064.77657 3759.31548 -3507.34293 6012.42055 7476.98599 14825.46096
## 68 69 70 71 72 73
## 1996.07961 -12931.35385 -1185.30967 4737.24218 -4773.69101 -4339.94254
## 74 75 76 77 78 79
## -10482.28871 2381.08536 -5450.62728 968.69268 -6938.65652 419.11978
## 80 81 82 83 84 85
## -2460.48355 -2808.16496 -4056.71686 -683.29915 2182.69211 3669.82998
## 86 87 88 89 90 91
## 431.84056 -519.01799 162.28635 4274.19938 -1146.09054 1155.02473
## 92 93 94 95 96 97
## -2049.50389 -1050.35525 162.81626 263.98381 -7490.45139 2315.78856
## 98 99 100 101 102 103
## -8645.73320 -3059.14494 -4170.19987 -1888.44750 -1409.61383 3040.14588
## 104 105 106 107 108 109
## -2434.45971 2491.65073 -1222.84017 903.68811 2538.39375 -3172.02832
## 110 111 112 113 114 115
## -4767.82082 -933.52666 1823.24242 11641.88479 -1176.72795 2714.79007
## 116 117 118 119 120 121
## 4328.98110 3601.29116 -980.12730 -4621.50994 -3685.30280 2318.99661
## 122 123 124 125 126 127
## -1710.85643 1343.61659 8874.23820 945.37018 224.84984 -2437.02182
## 128 129 130 131 132 133
## 2705.35862 7122.09551 1140.45028 -8377.46377 1775.86139 4175.83726
## 134 135 136 137 138 139
## -3089.12556 -1383.78518 -835.50351 -3871.44624 1154.33980 -508.87769
## 140 141 142 143 144 145
## -2929.50815 1677.20835 -1900.14256 -7863.26870 1936.23684 -3550.16766
## 146 147 148 149 150 151
## 2008.20716 -319.34862 966.93215 -398.24690 1315.16967 1167.46463
## 152 153 154 155 156 157
## 3351.45017 -4834.13097 -1195.82945 -3265.13796 5900.94505 9754.63575
## 158 159 160 161 162 163
## -3403.21618 -4767.50492 3586.59680 231.73980 2748.32579 -5821.80830
## 164 165 166 167 168 169
## -6707.59424 4146.11282 17439.71154 3841.90175 -162.30340 -2226.85528
## 170 171 172 173 174 175
## -918.48948 3759.08112 -32.10808 -7889.93624 2968.72114 4459.31002
## 176 177 178 179 180 181
## 797.62399 8922.38670 -9003.47416 -3325.31939 -10632.13294 -11222.97456
## 182 183 184 185 186 187
## 1164.36115 9258.85310 -1360.66541 5991.86227 6678.37531 13337.62260
## 188 189 190 191 192 193
## 8715.90754 -3729.03355 2731.13678 10633.31683 -1311.63271 -2158.86941
## 194 195 196 197 198 199
## -10041.81860 -6233.07994 1304.18069 -5147.93444 -9753.72142 5349.36111
## 200 201 202 203 204 205
## -3036.69226 -1697.23488 -791.45286 6512.54048 9965.97193 746.61169
## 206 207 208 209 210 211
## 3087.52389 3276.35870 5977.90559 13063.32935 -5366.24157 -11052.93963
## 212 213 214 215 216 217
## -5538.13370 -10510.58486 -5084.56644 1489.87359 -13015.05318 16289.38404
## 218 219 220 221 222 223
## 7875.38440 1664.32817 26832.09326 12886.77021 7764.05254 14471.97866
## 224 225 226 227 228 229
## -3397.92141 -1314.52831 4145.09581 723.89102 3078.68571 9330.90793
## 230 231 232 233 234 235
## 6205.63241 -1514.08965 -1488.17230 9720.18945 -11158.31999 -7063.12832
## 236 237 238 239 240 241
## -8398.01593 -10036.09331 3062.31989 1378.96907 -8248.17469 -9003.17568
## 242 243 244 245 246 247
## 9021.07839 -7738.50802 2459.12792 -10290.72192 -4117.72987 1347.02595
## 248 249 250 251 252 253
## 962.91759 -12328.86396 3542.24497 2018.47131 4203.76984 2175.57587
## 254 255 256 257 258 259
## -1094.88589 11198.89122 21036.17288 3512.05824 -3962.37548 4352.59477
## 260 261 262 263 264 265
## -1439.72957 3954.99275 -4621.58823 -10722.77444 -4658.24921 -487.84979
## 266 267 268 269 270 271
## -5151.76068 8778.67797 -4198.75578 4235.65771 -2023.27968 4496.25502
## 272 273 274 275 276 277
## 810.70699 7406.10214 -1255.91801 12157.71751 -4367.51510 1883.96123
## 278 279 280 281 282 283
## -213.70992 7993.78468 -4865.29747 -2595.42255 -11155.28496 -2653.21327
## 284 285 286 287 288 289
## 18659.25548 7939.42815 2936.44795 -424.82725 1085.59519 6568.59210
## 290 291 292 293 294 295
## 7086.02024 -18536.64003 -11064.28501 -8126.56385 9614.30325 3112.90589
## 296 297 298 299 300 301
## -1108.97596 27465.86292 10330.88039 5212.53579 9831.92681 3206.02551
## 302 303 304 305 306 307
## -700.56504 8185.28324 -23978.94429 -3422.76337 -89.92454 -6881.65061
## 308 309 310 311 312 313
## -3931.79833 2954.15737 -9136.86973 -3230.54115 -8191.69465 1521.65247
## 314 315 316 317 318 319
## -3160.09371 2035.92402 -4062.39286 27450.33395 -535.15211 3459.47066
## 320 321 322 323 324 325
## 11008.73209 5827.86415 32635.43831 5565.50567 -20497.27626 2041.36664
## 326 327 328 329 330 331
## 1350.40466 -6237.39317 -1568.31552 -33122.04848 833.80284 -2311.71498
## 332 333 334 335 336 337
## -90.89131 -3140.92458 4113.00216 -361.89193 -6866.43722 -3063.42182
## 338 339 340 341 342 343
## -2141.12584 -7625.02776 3873.92948 -1302.36793 -1662.92150 -916.07366
## 344 345 346 347 348 349
## 261.78411 580.40026 -1506.66883 -9337.51841 -13153.97708 2297.04649
## 350 351 352 353 354 355
## -4295.13984 -3637.43899 -5960.38416 1754.19447 1422.33637 2817.32908
## 356 357 358 359 360 361
## -3672.38607 -437.75451 763.23801 7113.02792 425.98881 112.05963
## 362 363 364 365 366 367
## 2732.23296 -2585.83912 -731.63260 -8601.45920 -4533.14135 -6135.87657
## 368 369 370 371 372 373
## -4895.97560 -7210.91877 5032.61828 439.48480 7203.62124 -7499.84988
## 374 375 376 377 378 379
## -2167.84016 -3295.25980 -2382.82764 -12374.81055 1925.04668 -10578.77810
## 380 381 382 383 384 385
## 5705.51375 9401.59529 3258.91544 -2245.55487 1742.89107 6892.74862
## 386 387 388 389 390 391
## 11596.66702 -5560.34813 -5175.69933 -12.72410 8703.57490 2000.82771
## 392 393 394 395 396 397
## 11406.53110 -9642.31849 2931.05253 877.08740 721.49131 -499.94525
## 398 399 400 401 402 403
## -420.48499 -14353.37192 8577.23634 -1062.35731 -1256.72459 7094.16976
## 404 405 406 407 408 409
## -7776.91665 -1186.42409 -2420.39523 -5713.24596 -2774.52862 -3833.96540
## 410 411 412 413 414 415
## -8679.05652 6177.24955 1737.24974 -7257.31670 -7608.51423 14276.30646
## 416 417 418 419 420 421
## 3962.45403 4656.23007 -7852.87443 -4610.20520 -2487.87795 2927.94124
## 422 423 424 425 426 427
## -13878.39690 -2729.23902 -9033.71965 3046.28995 7048.48811 6698.40985
## 428 429 430 431 432 433
## -3824.15452 -3979.31093 -4598.80536 -1684.29973 -5605.80602 -6541.34492
## 434 435 436 437 438 439
## -5886.73709 -1345.55457 -787.94281 -4902.17712 2642.95539 4931.91857
## 440 441 442 443 444 445
## -4926.26812 -2049.31645 1683.31274 -3711.52678 2947.33870 -6441.54868
## 446 447 448 449 450 451
## -12004.52607 -4461.86121 9689.74847 -1912.46075 4872.08448 -5715.46869
## 452 453 454 455 456 457
## -997.14637 512.01026 3165.09231 -12105.92465 3464.90468 -6572.89114
## 458 459 460 461 462 463
## 6620.79947 3160.93668 2677.86043 -3659.18415 2256.64977 171.15786
## 464 465 466 467 468 469
## 1971.74038 -332.09886 3535.02483 -2436.40000 5989.84211 -6724.31305
## 470 471 472 473 474 475
## -2790.00240 -2044.58153 -4510.00081 3130.21103 7957.51381 -5807.47713
## 476 477 478 479 480 481
## 1656.92147 -5994.14831 -2693.40825 2153.71790 -12768.05819 -9664.10719
## 482 483 484 485 486 487
## -1153.24363 84.19410 -878.45796 -1247.07473 -9482.79687 11154.22939
## 488 489 490 491 492 493
## 6380.09645 7609.09764 -5203.77354 5562.09451 9518.77945 6328.52632
## 494 495 496 497 498 499
## -13176.58608 -10363.60717 -3301.37714 -977.08588 -391.18043 -7485.60436
## 500 501 502 503 504 505
## 716.07670 4411.47247 5669.90553 860.36651 284.71678 -7034.68501
## 506 507 508 509 510 511
## 731.26545 -4877.07325 1978.86471 -1130.37420 -7993.71866 -480.61715
## 512 513 514 515 516 517
## -2544.11030 -462.20436 1466.06350 -9342.81512 -7662.26629 24357.50910
## 518 519 520 521 522 523
## 10060.98309 6167.85248 -5026.29961 3058.51782 17285.81589 11829.71257
## 524 525 526 527 528 529
## -23750.42724 -4836.47540 -3544.69115 4739.24937 -158.87097 -10908.10505
## 530 531 532 533 534 535
## 4519.76439 14073.95634 -4725.96448 4585.16924 5788.36807 -1531.64776
## 536 537 538 539 540 541
## -4303.92777 -6871.18358 -1940.86905 8475.40158 335.97267 -7934.21356
## 542 543 544 545 546 547
## 1973.13489 -425.55374 540.24335 -10851.15321 -10944.71502 2104.15870
## 548 549 550 551 552 553
## 7099.69204 -1164.01296 989.74340 -7555.63372 8689.42941 1093.77968
## 554 555 556 557 558 559
## -11749.95397 9285.69949 8846.18445 339.06290 5086.01547 -3319.43573
## 560 561 562 563 564 565
## 14331.96803 21799.83914 -6056.85752 -9344.20493 7030.96506 511.08471
## 566 567 568 569 570 571
## 3725.39232 -7099.19153 -17086.71819 6779.08204 6604.16955 2115.17577
## 572 573 574 575 576 577
## 3320.84832 2007.87131 -1921.93518 14940.97137 -9341.81984 -6012.73838
## 578 579 580 581 582 583
## 8900.22077 3100.37709 -6293.49782 7713.50631 -3551.36637 -2557.34589
## 584 585 586 587 588 589
## 15901.56242 -14206.57343 8618.92384 312.97816 -5975.53550 -559.26406
## 590 591 592 593 594 595
## 444.26319 -10457.01369 1931.11664 -6987.70788 3189.69685 9020.45135
## 596 597 598 599
## -7284.70908 6020.99135 2945.68455 7082.64306
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17304.77 20121.93 24331.05 24051.99 26380.55 23741.32 24450.09 19732.47
## 10 11 12 13 14 15 16 17
## 19471.81 16842.59 17611.86 14375.74 14426.36 15084.01 16762.70 15100.43
## 18 19 20 21 22 23 24 25
## 16124.19 15504.33 22512.09 21605.76 21091.11 22961.19 22294.28 22939.95
## 26 27 28 29 30 31 32 33
## 24766.44 18758.85 20466.80 28221.49 28278.53 27954.90 25609.30 26996.95
## 34 35 36 37 38 39 40 41
## 30799.47 31144.22 32538.53 30075.39 34087.78 37262.04 34349.18 31193.69
## 42 43 44 45 46 47 48 49
## 30053.62 20732.49 28171.93 30582.72 31660.44 38426.44 37926.35 42539.38
## 50 51 52 53 54 55 56 57
## 46730.97 39505.88 34131.61 29206.81 22423.52 28647.20 25263.90 21600.54
## 58 59 60 61 62 63 64 65
## 25962.41 27208.68 27502.56 27910.04 23824.51 40240.83 42065.34 37361.44
## 66 67 68 69 70 71 72 73
## 41524.01 46387.82 56943.49 54978.21 40377.02 37909.19 40895.26 35255.51
## 74 75 76 77 78 79 80 81
## 30755.72 21557.20 24724.91 20693.59 22757.66 17707.02 19701.20 18935.88
## 82 83 84 85 86 87 88 89
## 17973.86 16063.16 17327.45 20897.46 25268.59 26248.02 26272.71 26882.94
## 90 91 92 93 94 95 96 97
## 30964.52 29807.40 30796.22 28881.07 28089.33 28453.59 28855.88 22501.07
## 98 99 100 101 102 103 104 105
## 25484.30 18588.29 17456.49 15517.88 15814.47 16484.71 20910.17 20003.35
## 106 107 108 109 110 111 112 113
## 23477.41 23269.60 24928.03 27774.46 25298.96 21779.96 22052.47 24670.83
## 114 115 116 117 118 119 120 121
## 35420.73 33632.64 35450.73 38417.42 40352.70 38065.51 32941.16 29321.15
## 122 123 124 125 126 127 128 129
## 31382.00 29680.10 30849.19 38368.77 38015.01 37086.45 33983.07 35745.48
## 130 131 132 133 134 135 136 137
## 41086.41 40532.61 31827.14 33078.59 36234.70 32683.21 31087.50 30182.16
## 138 139 140 141 142 143 144 145
## 26775.52 28175.02 27947.08 25657.79 27660.86 26300.13 19969.76 22968.31
## 146 147 148 149 150 151 152 153
## 20817.94 23763.63 24297.92 25871.53 26051.69 27688.39 28975.41 31975.56
## 154 155 156 157 158 159 160 161
## 27493.54 26764.28 24345.34 30177.22 41423.64 39771.50 37164.26 42131.55
## 162 163 164 165 166 167 168 169
## 43525.25 46905.09 42418.88 37775.60 43143.57 59273.67 61462.45 59893.28
## 170 171 172 173 174 175 176 177
## 56752.49 55168.63 57842.68 56877.08 49250.56 52044.26 55747.38 55783.18
## 178 179 180 181 182 183 184 185
## 62836.76 53439.32 50224.56 41130.26 32758.92 36230.15 46226.95 45688.71
## 186 187 188 189 190 191 192 193
## 51578.62 57262.95 67932.09 73159.18 66920.43 67111.83 74107.49 69829.58
## 194 195 196 197 198 199 200 201
## 65399.68 54757.08 48850.25 50259.51 45900.72 38152.21 44509.12 42755.23
## 202 203 204 205 206 207 208 209
## 42397.02 42870.32 49592.60 58387.96 58021.48 59728.07 61366.38 65117.53
## 210 211 212 213 214 215 216 217
## 74484.10 66650.51 54964.28 49630.01 40721.42 37711.27 40792.05 30917.62
## 218 219 220 221 222 223 224 225
## 47711.90 54955.39 55847.76 78372.80 85788.66 87770.74 95281.92 86328.39
## 226 227 228 229 230 231 232 233
## 80390.19 79976.54 76661.89 75832.23 80519.22 81869.09 76363.32 71626.81
## 234 235 236 237 238 239 240 241
## 77220.75 64009.56 56130.16 48165.81 39865.97 44013.60 46143.60 39663.46
## 242 243 244 245 246 247 248 249
## 33409.78 43583.65 37891.30 41785.44 34131.02 32850.55 36467.23 39261.29
## 250 251 252 253 254 255 256 257
## 30187.61 36062.96 39824.23 44964.14 47653.74 47151.68 57343.83 74656.23
## 258 259 260 261 262 263 264 265
## 74473.23 67854.55 69320.73 65581.44 67012.30 60835.92 50223.82 46293.14
## 266 267 268 269 270 271 272 273
## 46500.33 42648.18 51359.33 47671.77 51774.71 49911.17 53935.58 54228.47
## 274 275 276 277 278 279 280 281
## 60182.35 57841.57 67412.37 61401.32 61609.14 59975.64 65657.87 59454.57
## 282 283 284 285 286 287 288 289
## 56054.71 45717.36 44131.03 61181.29 66652.98 67058.11 64502.98 63599.98
## 290 291 292 293 294 295 296 297
## 67558.69 71427.64 52624.86 42831.42 36905.70 47118.09 50325.69 49448.99
## 298 299 300 301 302 303 304 305
## 73389.83 79272.46 79933.07 84496.83 82714.42 77797.15 81227.37 56391.19
## 306 307 308 309 310 311 312 313
## 52691.78 52374.94 46230.66 43469.56 47034.87 39665.68 38401.27 33020.20
## 314 315 316 317 318 319 320 321
## 36764.81 35954.79 39745.82 37751.52 63265.72 61129.67 62736.13 70649.85
## 322 323 324 325 326 327 328 329
## 73011.99 98224.78 96619.56 72704.78 71515.31 69889.96 61926.60 59079.19
## 330 331 332 333 334 335 336 337
## 29344.63 32993.29 33428.18 35723.64 35071.43 40777.61 41841.87 37139.56
## 338 339 340 341 342 343 344 345
## 36362.27 36487.60 31855.93 37791.65 38448.06 38703.79 39570.36 41337.46
## 346 347 348 349 350 351 352 353
## 43140.24 42894.52 35913.55 26580.81 31869.14 30742.15 30336.53 27978.09
## 354 355 356 357 358 359 360 361
## 32607.66 36322.39 40738.96 38947.04 40194.05 42309.97 49627.30 50172.08
## 362 363 364 365 366 367 368 369
## 50371.62 52808.84 50318.78 49769.17 42491.86 39718.16 35935.40 33737.49
## 370 371 372 373 374 375 376 377
## 29836.81 37047.94 39310.81 47113.28 41148.41 40601.40 39154.11 38691.81
## 378 379 380 381 382 383 384 385
## 29655.67 34205.35 27330.20 35462.98 45687.23 49215.13 47506.68 49477.39
## 386 387 388 389 390 391 392 393
## 55632.05 65017.63 58300.41 52826.87 52558.43 59860.32 60378.18 68955.60
## 394 395 396 397 398 399 400 401
## 58175.95 59726.34 59291.08 58780.37 57283.20 56057.80 42955.76 51451.07
## 402 403 404 405 406 407 408 409
## 50462.01 49439.12 55773.06 48394.00 47712.40 46056.67 41779.39 40622.39
## 410 411 412 413 414 415 416 417
## 38706.63 32862.89 40652.89 43548.46 38276.80 33416.69 48131.97 51936.34
## 418 419 420 421 422 423 424 425
## 55824.30 48372.63 44734.59 43424.49 46973.25 35514.10 35246.15 29565.28
## 426 427 428 429 430 431 432 433
## 35096.37 43336.45 50156.15 46955.60 44055.09 41012.59 40901.95 37416.77
## 434 435 436 437 438 439 440 441
## 33595.74 30858.84 32418.37 34248.32 32273.90 37088.94 43229.27 40015.75
## 442 443 444 445 446 447 448 449
## 39724.83 42699.67 40607.95 44555.55 39852.38 30978.86 29828.54 41066.18
## 450 451 452 453 454 455 456 457
## 40751.06 46342.90 42024.86 42370.85 43974.34 47653.50 37634.10 42432.46
## 458 459 460 461 462 463 464 465
## 37903.77 45393.35 48876.43 51469.47 48233.35 50549.56 50748.97 52477.67
## 466 467 468 469 470 471 472 473
## 51980.55 54893.40 52249.73 57247.88 50578.57 48214.58 46815.57 43475.36
## 474 475 476 477 478 479 480 481
## 47192.06 54577.05 49062.51 50747.86 45591.41 43987.42 46790.63 36315.96
## 482 483 484 485 486 487 488 489
## 29945.10 31794.81 34463.17 35937.50 36893.23 30600.77 42999.47 49589.76
## 490 491 492 493 494 495 496 497
## 56348.34 51115.33 55897.65 63451.19 67222.59 53623.18 44299.95 42345.66
## 498 499 500 501 502 503 504 505
## 42665.47 43448.32 37992.92 40366.67 45612.52 51234.49 51936.71 52046.11
## 506 507 508 509 510 511 512 513
## 45814.16 47140.07 43438.56 46165.09 45834.29 39616.05 40735.25 39919.06
## 514 515 516 517 518 519 520 521
## 41013.08 43625.39 36540.69 31869.63 55508.45 63583.43 67198.01 60646.63
## 522 523 524 525 526 527 528 529
## 61972.04 75415.00 82318.43 57531.76 52455.69 49184.75 53517.73 53029.25
## 530 531 532 533 534 535 536 537
## 43315.95 48255.33 60782.82 55361.26 58723.20 62669.08 59752.64 54835.61
## 538 539 540 541 542 543 544 545
## 48366.58 47036.60 54890.31 54643.36 47281.58 49481.84 49310.33 49996.87
## 546 547 548 549 550 551 552 553
## 40744.14 32665.70 36961.88 44993.16 44792.26 46480.21 40553.00 49471.22
## 554 555 556 557 558 559 560 561
## 50614.38 40501.01 49941.67 57721.79 57093.41 60653.29 56465.03 68101.88
## 562 563 564 565 566 567 568 569
## 84615.00 74810.20 63494.03 67866.77 66010.89 67185.05 58843.72 43001.20
## 570 571 572 573 574 575 576 577
## 49936.12 55779.11 56949.44 59003.13 59643.36 56800.03 68917.82 58403.02
## 578 579 580 581 582 583 584 585
## 52192.06 59713.62 61201.78 54368.49 60569.08 56191.77 53267.44 66694.72
## 586 587 588 589 590 591 592 593
## 52276.65 59543.59 58645.54 52433.84 51746.31 52019.44 42833.03 45600.42
## 594 595 596 597 598 599
## 40283.45 44484.55 53155.57 46557.01 52354.32 54707.07
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8345
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.015816 0.5559992 3.485853
## t2* 1741.195183 23.8850786 221.068427
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.672356 7.163808 13.89931
## 2 lag_depvar 1427.002146 1751.477983 2153.45054Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jul 10 01:09:00 2023
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 2.117667 | 5.410333 | 5.629750 | 6.3588810 |
| Comida | 331.003167 | 310.278417 | 314.087500 | 339.0315000 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 37.639167 | 47.072333 | 38.297667 | 33.1484286 |
| Enceres | 26.875333 | 20.086417 | 17.443792 | 24.9535000 |
| Farmacia | 3.330000 | 1.831667 | 7.913875 | 9.0084286 |
| Gas/Bencina | 32.671333 | 44.325000 | 28.954333 | 26.1436190 |
| Diosi | 13.981667 | 31.180667 | 41.934250 | 36.5659048 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 6.5409286 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 19.7492381 |
| VTR | 10.996667 | 25.156667 | 22.121792 | 19.6730476 |
| Netflix | 4.753333 | 7.151583 | 7.090167 | 7.0869286 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.9003095 |
| Total | 463.368333 | 499.588167 | 522.488250 | 529.1607143 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2016, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-07-09 00:04:58 sería de: 36.211 pesos// Percentil 95% más alto proyectado: 39.296,52
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36099.42 | 36099.04 |
| Lo.80 | 36100.13 | 36099.89 |
| Point.Forecast | 36210.70 | 36457.90 |
| Hi.80 | 37949.10 | 41244.04 |
| Hi.95 | 38902.89 | 43777.66 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3585 1000.613
## s.e. 0.1334 33.721
##
## sigma^2 = 26098: log likelihood = -343.75
## AIC=693.5 AICc=693.99 BIC=699.41paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.4023 32.6482
## s.e. 0.1262 1.1794
##
## sigma^2 = 26193: log likelihood = -343.86
## AIC=693.73 AICc=694.22 BIC=699.64forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 923.8927 | 661.4355 | 654.2892 |
| Lo.80 | 1043.8185 | 778.8363 | 729.7131 |
| Point.Forecast | 1270.3638 | 1000.6115 | 896.7250 |
| Hi.80 | 1496.9092 | 1222.3868 | 1209.7849 |
| Hi.95 | 1616.8350 | 1339.7875 | 1417.5933 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.28 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.2 xts_0.13.1
## [13] forecast_8.21 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.2 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
## [28] sjPlot_2.8.14 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.2 httr_1.4.6
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.8 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.3
## [16] timechange_0.2.0 anytime_0.3.9 tseries_0.10-54
## [19] colorspace_2.1-0 xfun_0.39 crayon_1.5.2
## [22] jsonlite_1.8.4 lme4_1.1-34 glue_1.6.2
## [25] gtable_0.3.3 emmeans_1.8.7 sjstats_0.18.2
## [28] sjmisc_2.8.9 car_3.1-2 quantmod_0.4.23
## [31] abind_1.4-5 mvtnorm_1.2-2 DBI_1.1.3
## [34] ggeffects_1.2.3 Rcpp_1.0.10 viridisLite_0.4.2
## [37] xtable_1.8-4 performance_0.10.4 bit_4.0.5
## [40] htmlwidgets_1.6.2 timeSeries_4030.106 gplots_3.1.3
## [43] ellipsis_0.3.2 spatial_7.3-14 pkgconfig_2.0.3
## [46] farver_2.1.1 nnet_7.3-16 sass_0.4.5
## [49] dbplyr_2.3.3 janitor_2.2.0 utf8_1.2.3
## [52] tidyselect_1.2.0 labeling_0.4.2 rlang_1.1.0
## [55] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [58] cachem_1.0.7 cli_3.6.1 generics_0.1.3
## [61] sjlabelled_1.2.0 broom_1.0.5 evaluate_0.20
## [64] fastmap_1.1.1 yaml_2.3.7 knitr_1.43
## [67] bit64_4.0.5 caTools_1.18.2 nlme_3.1-153
## [70] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [73] compiler_4.1.2 rstudioapi_0.14 curl_5.0.0
## [76] bslib_0.4.2 highr_0.10 fBasics_4022.94
## [79] Matrix_1.6-0 its.analysis_1.6.0 nloptr_2.0.3
## [82] urca_1.3-3 vctrs_0.6.1 pillar_1.9.0
## [85] lifecycle_1.0.3 lmtest_0.9-40 jquerylib_0.1.4
## [88] estimability_1.4.1 bitops_1.0-7 insight_0.19.3
## [91] R6_2.5.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [94] codetools_0.2-18 assertthat_0.2.1 boot_1.3-28
## [97] MASS_7.3-54 gtools_3.9.4 withr_2.5.0
## [100] fracdiff_1.5-2 bayestestR_0.13.1 parallel_4.1.2
## [103] hms_1.1.3 quadprog_1.5-8 timeDate_4022.108
## [106] minqa_1.2.5 snakecase_0.11.0 rmarkdown_2.23
## [109] carData_3.0-5 TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))